Toward a new conceptualization of resilience at work as a meta-construct?
Bibliographic record
Abstract
Organizations of all kinds are faced with multiple demands for adaptation of increasing frequency and amplitude due to such factors as reorganizations, climate change, pandemics, and labor shortages. This new reality requires our organizations to anticipate, adjust, and demonstrate resilience. The study of resilience at work relies on the comprehension of how organizational systems, as well as their work collectives and members, manage to overcome adversity without suffering from irreversible damage. However, the study of this phenomenon of interest contains grey areas concerning both its definition, its conceptualization, and the dynamic processes that underlie it. This theoretical paper addresses these different issues by providing first, a conceptual content analysis of the most frequently used definitions and second, a new conceptualization of resilience at work as a resource, either individual or collective. Moreover, we suggest a multilevel, dynamic, and virtuous conceptual approach to resilience at work, relying on both bottom-up and top-down flows. Accordingly, we formulate different theoretical propositions upon which future empirical research can draw to analyze the relationships between individual, team, and organizational resilience. Building on a conservation of resources lens, we offer a novel contribution to the resilience in the workplace literature, by providing an integrative and multilevel theory of resilience at work that highlights both the processual and interpersonal nature of its emergence, and the organizational levers that can foster it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.041 |
| Scholarly communication | 0.012 | 0.033 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".